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Table of Contents Introduction Putting the 10x Claim Into Perspective How Did We Get Here? This Was an Extensive Evaluation The Priority Was Compatibility Why Not Rust? Why Not C#? Why Go Fit the Existing Compiler A Port, Not a Simple Translation Where Does the Performance Come From? Native Execution Parallel Processing Memory Efficiency and Larger Projects The Benchmarks Memory Usage The JavaScript API Trade Off Is It Still TypeScript? The F1 Analogy Large Companies Helped Test TypeScript 7 Should You Upgrade? Final Thoughts Introduction At the end of March 2025, I published this article: Go-ing Beyond TypeScript: Microsoft Picks Go: How Will This Change the Landscape? Giorgi Kobaidze Giorgi Kobaidze Giorgi Kobaidze Follow Mar 31 '25 Go-ing Beyond TypeScript: Microsoft Picks Go: How Will This Change the Landscape? # microsoft # typescript # go # csharp 1 reaction 2 comments 12 min read At the time, Microsoft's decision to port the TypeScript compiler to Go sparked quite a bit of discussion and controversy. Many people questioned whether moving such a critical piece of the ecosystem away from TypeScript was the right choice. And boy, did Microsoft deliver what it promised: an order-of-magnitude performance improvement on some of the world's largest TypeScript codebases. The results are here, and the benchmarks speak for themselves. Putting the 10x Claim Into Perspective The phrase "10x faster" describes the scale of the improvement Microsoft has demonstrated. It does not guarantee that every codebase will become exactly ten times faster. The results depend on the size of the project, the work being performed, and the available hardware. Some projects might see a 5x improvement, while others could reach 8x, 10x, 12x, or potentially even more. No, this doesn't make every TypeScript developer a 10x developer , But it does mean that compiling a TypeScript project, loading it in an editor, and receiving diagnostics could become dramatically faster after moving to the nat

If you subscribe to more newsletters than you read, which tool fixes it depends entirely on which problem you actually have. Most roundups skip that step and just rank apps. Disclosure up front: we make one of the eight tools below. It's the last entry, it's new, and it has no track record — its section says so plainly. The other seven are real options and for most people one of them is the better pick. Every price and behaviour here was checked against the vendor's own site on 2 August 2026 . Where a vendor doesn't publish a price, this says that instead of guessing. The two problems people both call "too many newsletters" They aren't the same problem, and the tools split cleanly along the seam. Clutter. Newsletters are burying your real email. You'd read them, you just don't want them sitting next to your bank and your on-call alerts. The fix is routing: move them somewhere else. Volume. Twenty-five arrive a week and you have time for three. Moving them changes nothing — now you have twenty-five unread items in a nicer app. The fix is either condensing the pile or deciding what's in it. Almost every tool below solves exactly one of these. Buying a clutter tool for a volume problem is the standard way to end up paying a subscription and still having the same unread count. The plumbing, since you're the one wiring it up Four mechanics show up across all eight: Dedicated intake addresses. Readwise Reader, Meco, Readless and Digest each hand you an address on their domain (Meco's look like you@mecoinbox.com ). You subscribe with it and their infrastructure receives the mail — the cleanest integration point available: no OAuth scope on your mailbox, no IMAP polling, no shared credentials. Mailbox connection. Meco will alternatively connect Gmail or Outlook and pull your existing subscriptions across, setting the selected ones to skip your inbox (reversible at any time, per Meco's FAQ). Much faster than re-subscribing to 25 newsletters by hand. The cost is a read scope

I hit Ctrl-F more than any other key combination on this machine. It runs a shell function called fts — "find tmux session," which is not a good name but it's four years too late to change it. I press it, a fuzzy finder opens listing every project directory I have, I type a few characters, and I'm sitting in a tmux session for that project with the panes already laid out. If the session already existed, I'm back in it exactly where I left off. If what I typed doesn't exist yet, it offers to create it. Somebody watched me do this over a screen share recently and asked what was going on. So: here's the whole thing, the four tools it's built on, and a breakdown of every part that isn't obvious. What you'll end up with: One keystroke from anywhere to any project Fuzzy search across every repo you own, with a live directory tree preview Type a name that doesn't exist → it offers to scaffold and place it Never accidentally start a second tmux session for a project you already have open The same window/pane layout in every project, every time The problem it solves Before this, starting work looked like: cd ~/repo/work/some-project-i-half-remember-the-name-of tmux new-session -s some-project # split some panes, badly, slightly differently each time Three or four commands, one of which needed me to remember a path. None of it hard. All of it friction at exactly the wrong moment — the moment you've decided to start something, which is the moment you're most likely to get distracted instead. I'd also collected tmux sessions named 0 , 1 , 2 and some-project-2 , because I kept starting new ones instead of attaching to the one already running. So the goal wasn't really speed. It was making the right thing the automatic thing. Prerequisites Four tools plus zsh. All four are worth having on their own, and three of them are things you'll reach for daily once installed. Tool Version I'm on What it does here tmux 3.6a The terminal multiplexer. Holds the sessions, windows and panes. fz

The start of a new school year can feel hectic, as parents juggle their kids’ classes, extracurriculars and sports on top of work, appointments, and other responsibilities. It’s easy for things to slip through the cracks, but Skylight’s shared smart calendars can help keep everyone on track, and from August 2nd through August 9th they’re […]

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Struggling with audio dropout or finnicky pairing? Ensure these common Bluetooth speaker problems aren't getting in the way of your auditory delights.

The HP HyperX Omen 15, which I first saw at CES, replaces the HP Victus 15, a longtime bestselling budget gaming laptop. The Victus cost just $800, or less when on sale, and was a good entry point to decent laptop gaming. The Omen 15 has nice little upgrades over the Victus. Its RTX 5050 […]

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Using your phone while it's plugged in changes the way it charge. Here are some common myths (and truths) about charging smartphones and tablets.

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Keep the shell. Preserve the presence. The ally doesn't suspect; the enemy doesn't move. — The 36 Stratagems, Slough off the Cicada's Golden Shell Previously on this series: #19: Mark Found His AI Audit Method in a Training Manual. He Left a Trap in His Report. — P confirmed Mark's report was read from a Singapore IP. A note was left: "Entry's gone. Two weeks. Don't reach out. I'll find you." #20: Alex Felt the AI Collector Slow Down. He Knew Someone Else Had Made a Move. — ACL's processing latency climbed abnormally. Someone had done something in the same time window. Exposed P's monitoring pinged while P was still helping Mark verify an address. Deep night. The screen was the only light in the room. P opened the monitor. The record was waiting: a read from Singapore. Time, method, address, all matching. Mark's bait had been taken. P knew this path. A false lead planted in Mark's report, waiting for this exact day. P double-checked the address: an AWS Elastic IP registered in the Singapore region, same network block. No ambiguity. P sent an encrypted message: "Your report was read. From a Singapore IP." Then P ran the routine check. The environment status list scrolled in the terminal: storage levels, certificate expiry, key rotation dates. P had read these lines a hundred times. Every time, identical. One line was different. P's fingers stopped on the trackpad. The cursor sat on the entry's metadata line. A new tag P had never configured. # Old entry metadata: new entry (not configured by P) status : reclaim_pending source : acl-asset-scanner scanned_at : 02:01:07Z P didn't move. The cursor sat on screen. In the room, only the fan. The fan cycled once. P's fingers lifted off the trackpad, then settled back. The tag was still there. The tag wasn't an alert. Not an error, no explanation. The format matched ACL's automated scan records. P had seen it before, in a data company's audit report last year, in another client's logs the year before. ACL's scanner had swept

Learning CSS can feel like magic, but it can also be incredibly frustrating. One minute your website looks perfect, and the next minute, a single line of code breaks the entire layout.If you are struggling to get your web pages to look exactly how you want, don't worry. Here are 5 of the most common CSS mistakes beginners make and exactly how you can fix them. 1. Forgetting the CSS Box Model (Adding Padding Breaks Width) The Mistake : You set a box's width to 100%, but as soon as you add padding: 20px; or a border, horizontal scrollbars appear and your layout breaks.Why it happens: By default, CSS adds padding and borders on top of the width you specified. So, 100% width + 20px padding left + 20px padding right = wider than the screen!The Fix: Always use box-sizing: border-box; at the top of your CSS file. This forces the browser to include padding and borders inside the specified width. /* Add this to the very top of your CSS file */ { box-sizing: border-box; margin: 0; padding: 0; } 2. Confusing Block vs. Inline Elements The Mistake: You try to add a vertical margin, width, or height to a or an tag, but nothing changes on the screen.Why it happens: Tags like , , and are inline elements. By default, inline elements ignore top/bottom margins, heights, and widths.The Fix: Change the element's display property to inline-block or block. /* Fix: This will now respect your width and margin settings */ a { display: inline-block; width: 150px; margin-top: 20px; } 3. Overusing Absolute Positioning (position: absolute) The Mistake: Using position: absolute; to push elements around the screen until they look "perfect" on your laptop, only to find the layout completely scrambled on a mobile screen.Why it happens: Absolute positioning takes elements out of the normal document flow. It makes your website completely rigid and unresponsive.The Fix: Stop using absolute positioning for general layouts. Instead, learn and use CSS Flexbox or CSS Grid to build flexible layouts. /* Inst

AI search is creating an attribution problem for marketers: a brand can help shape an answer in ChatGPT, Google AI Mode , or Perplexity without receiving a visit to its website. That makes rankings, impressions, and click-through rates incomplete indicators of visibility. New research from Wix Studio adds evidence that the content cited by AI systems follows recognizable patterns, while industry discussions increasingly point to measurement frameworks built around citations, answer presence, prompt coverage, and downstream influence. The key shift is not that website traffic has stopped mattering. It is that a click is no longer the only observable outcome of search visibility. When an AI interface summarizes options, recommends a product category, or cites a publisher, users may form an opinion or continue their journey elsewhere. Brands therefore need to separate direct referral traffic from their broader presence in AI-generated answers. What Wix Studio's research shows about AI citations Wix Studio's AI Search Lab research examines citations in answers generated by major AI search interfaces, including ChatGPT, Google AI Mode, and Perplexity. Published summaries describe a dataset of roughly 75,000 AI-generated answers and more than one million citations. Its central finding is that citations are not spread evenly across every kind of web page. Listicles, articles, and product pages account for a disproportionate share of the citations observed in the research. That is consistent with how answer engines retrieve and synthesize material: content that is clear, segmented, easy to scan, and closely matched to a question can be easier to extract into a response. A subsequent Search Engine Land summary of Wix Studio's work discussed a 25,000-URL dataset in which listicles represented a majority of AI citations. The precise mix should not be treated as a universal rule. Wix Studio's analysis covers a defined set of prompts and engines, and results can change with the

React TypeScript Property Form Validation export interface PropertyForm { propertyTitle : string ; description : string ; amenities : string ; monthlyRent : string ; location : string ; unitsAvailable : string ; applicationDeadline : string ; } export interface PropertyFormErrors { propertyTitle ?: string ; description ?: string ; amenities ?: string ; monthlyRent ?: string ; location ?: string ; unitsAvailable ?: string ; applicationDeadline ?: string ; } export const validatePropertyField = ( name : keyof PropertyForm , value : string ): string => { switch ( name ) { case " propertyTitle " : if ( ! value . trim ()) { return " Property title is required " ; } if ( value . trim (). length < 3 ) { return " Property title must be at least 3 characters " ; } return "" ; case " description " : if ( ! value . trim ()) { return " Description is required " ; } if ( value . trim (). length > 2000 ) { return " Description cannot exceed 2000 characters " ; } return "" ; case " amenities " : if ( ! value . trim ()) { return " Amenities are required " ; } return "" ; case " monthlyRent " : if ( ! value . trim ()) { return " Monthly rent is required " ; } if ( Number ( value ) <= 0 ) { return " Monthly rent must be greater than 0 " ; } return "" ; case " location " : if ( ! value . trim ()) { return " Location is required " ; } return "" ; case " unitsAvailable " : if ( ! value . trim ()) { return " Units available is required " ; } if ( ! Number . isInteger ( Number ( value ))) { return " Units available must be a whole number " ; } if ( Number ( value ) < 1 ) { return " At least 1 unit must be available " ; } return "" ; case " applicationDeadline " : if ( ! value ) { return " Application deadline is required " ; } return "" ; default : return "" ; } }; export const validatePropertyForm = ( formData : PropertyForm ): PropertyFormErrors => { const errors : PropertyFormErrors = {}; Object . entries ( formData ). forEach (([ name , value ]) => { const error = validatePropertyFiel

PDF merge, split, and compress sound like the most boring possible features to build. Take some files, do an operation, return a file. I believed that too, until real user files started hitting the backend and every one of these tools broke in a different, specific way. Here's what actually went wrong, and what fixed it. The PDF that wasn't actually a PDF The first crash report was a "corrupted file" error on a PDF that opened fine in every desktop viewer. Turns out plenty of real-world PDFs are technically malformed, a missing xref table, a truncated stream, an object reference pointing at nothing but viewers like Chrome and Acrobat are extremely forgiving about it. Most Python PDF libraries are not. try : reader = PdfReader ( file_path , strict = False ) except PdfReadError : # strict=False alone doesn't save you from everything — # some files need the xref table rebuilt from scratch reader = PdfReader ( file_path , strict = False ) reader . _override_encryption = True strict=False fixed maybe 70% of the "corrupted" reports. The rest needed a repair pass first — scanning the raw byte stream for object markers and reconstructing a valid cross-reference table before the normal parser ever touches it. Painful to write, but it turned "please fix your PDF" into "it just works," which matters a lot when the whole pitch of the tool is "no signup, just upload and go." Merging PDFs is not free, memory-wise The naive merge implementation loads every input PDF fully into memory, concatenates pages, writes the output. Fine for two 200KB files. Not fine when someone merges fifteen scanned documents at 40MB each, because now you're holding the equivalent of 600MB of parsed PDF objects in memory at once on a backend container that doesn't have unlimited RAM. The fix was switching to incremental writes process one input file at a time, write its pages to the output stream, then explicitly drop the reference before moving to the next file: writer = PdfWriter () for path in input_p

So, here's the thing. A few months ago, I kept hearing these four words everywhere — AI, machine learning, deep learning, generative AI — and honestly? I just nodded along like I knew what they meant. I didn't. Not really. Then I actually sat down and learned them properly, and it turns out they're way simpler than people make them sound. So here's my attempt at explaining them the way I wish someone had explained them to me. No scary maths, no fifty-page research papers. Just the actual ideas. First, the one thing everyone gets wrong These four terms are NOT the same thing. They're more like Russian dolls — each one fits inside the bigger one: AI is the biggest doll. The whole concept. Machine learning is inside AI. Deep learning is inside machine learning. Generative AI is a specific use of deep learning. Once I saw it like that, everything else clicked into place. AI: the big umbrella Artificial intelligence is basically any system that does something we'd normally say requires human thinking. That's it. That's the definition. And here's the part that surprised me — AI is old. Like, really old. The chess computer that beat Kasparov in 1997? That's AI. The enemy characters in old video games that chase you around? Technically AI. Most of that stuff doesn't "learn" anything. A programmer just wrote a bunch of rules, like "if the player is close, move towards them." So AI ≠ robots taking over the world. Most AI is honestly pretty boring. It's spam filters, autocorrect, and the thing that recommends which video plays next. Machine Learning: where it gets interesting This is where computers stopped following rules and started finding them. The classic example: a spam filter. The old way, a programmer would write rules like "if the email contains the word FREE!!! in all caps, it's spam." But spammers just change their spelling and the rules break. It's a never-ending game of cat and mouse. Machine learning flips it around. Instead of writing rules, you show the compute

On Windows I kept launching background servers from a task runner and watching them die the instant the launching step finished — no error, no log, just gone. The task runner was wrapping everything in a job object, and job-object teardown kills every child process on return. Nothing I did inside the child mattered; its death warrant was signed by how it was born. The workaround was to have the process created by something that outlives the runner — the OS scheduler, a WMI process-create call — instead of spawning it as a doomed descendant. When a process keeps dying without a trace, look at its lineage before its code — some parents kill their children on the way out, and no amount of hardening inside the child fixes how it was spawned.

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on smartphones and Android, follow Dominic Preston. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started Believe it or not, it didn't all start with […]

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